The code below estimates a logit model via rms::lrm(), then computes cluster-robust standard errors using rms::robcov().

```
library(broom)
library(rms)
library(effects)
fit <- lrm(degree ~ religion + gender + age, x=T, y=T, data=WVS)
fit_clus <- robcov(fit, cluster= WVS$country)
fit_clus
tidy(fit_clus)
```

I would like to tidy the resulting estimates for use by huxtable() or some other package, but the attempt at tidying generates an error as below.

```
> fit_clus
Logistic Regression Model
lrm(formula = degree ~ religion + gender + age, data = WVS, x = T,
y = T)
Model Likelihood Discrimination Rank Discrim.
Ratio Test Indexes Indexes
Obs 5381 LR chi2 46.01 R2 0.013 C 0.561
no 4238 d.f. 3 g 0.264 Dxy 0.122
yes 1143 Pr(> chi2) <0.0001 gr 1.302 gamma 0.123
Cluster on WVS$country gp 0.043 tau-a 0.041
Clusters 4 Brier 0.166
max |deriv| 9e-11
Coef S.E. Wald Z Pr(>|Z|)
Intercept -0.7886 0.3122 -2.53 0.0115
religion=yes 0.1124 0.1316 0.85 0.3930
gender=male -0.0768 0.1148 -0.67 0.5035
age -0.0132 0.0028 -4.78 <0.0001
>
> tidy(fit_clus)
Error in as.data.frame.default(x) :
cannot coerce class "c("lrm", "rms", "glm")" to a data.frame
>
```

I know that rms:lrm() is not on the list of **available tidiers** in the broom package, so I'm not surprised tidy(fit_clus) returns an error, but does anyone have strategies for extracting the regression table elements from the fit_clus object?

Thanks.